As a data analyst at Netflix supporting the Public Relations team, I have analyzed Netflix’s public Top 10 data to identify and quantify recent successes in original content. This analysis reveals compelling stories about Netflix’s global reach, the enduring power of flagship series like Stranger Things, and the company’s successful expansion into international markets, particularly India.
The key findings demonstrate Netflix’s evolution from a US-centric platform to a truly global entertainment ecosystem, with non-English content now representing a significant portion of viewing hours and local productions driving subscriber growth in key markets.
Data Acquisition and Preparation.
Task 1: Data Acquisition
This analysis utilizes Netflix’s publicly available Top 10 datasets covering both global and country-specific viewing patterns. The data provides weekly rankings, viewership hours, and cumulative performance metrics across Netflix’s programming catalog.
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# Making sure I have a place to store everythingif(!dir.exists(file.path("data", "mp01"))){dir.create(file.path("data", "mp01"), showWarnings=FALSE, recursive=TRUE)}# Getting the global dataGLOBAL_TOP_10_FILENAME <-file.path("data", "mp01", "global_top10_alltime.tsv")if(!file.exists(GLOBAL_TOP_10_FILENAME)){download.file("https://www.netflix.com/tudum/top10/data/all-weeks-global.tsv", destfile=GLOBAL_TOP_10_FILENAME)}# And the country-specific dataCOUNTRY_TOP_10_FILENAME <-file.path("data", "mp01", "country_top10_alltime.tsv")if(!file.exists(COUNTRY_TOP_10_FILENAME)){download.file("https://www.netflix.com/tudum/top10/data/all-weeks-countries.tsv", destfile=COUNTRY_TOP_10_FILENAME)}
Data Cleaning and Import
The datasets required minimal preprocessing, primarily converting Netflix’s “N/A” string values to proper NA values for accurate analysis.
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if(!require("tidyverse")) install.packages("tidyverse")library(readr)library(dplyr)# Import the global dataGLOBAL_TOP_10 <-read_tsv(GLOBAL_TOP_10_FILENAME, show_col_types =FALSE)# Take a look at what we're working withglimpse(GLOBAL_TOP_10)
# Fix those pesky "N/A" stringsGLOBAL_TOP_10 <- GLOBAL_TOP_10 |>mutate(season_title =if_else(season_title =="N/A", NA_character_, season_title))# Check that it workedcat("Season title NA values:", sum(is.na(GLOBAL_TOP_10$season_title)), "out of", nrow(GLOBAL_TOP_10), "rows\n")
Season title NA values: 4464 out of 8680 rows
Task 3: Data Import with Proper NA Handling
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# Same deal for country dataCOUNTRY_TOP_10 <-read_tsv(COUNTRY_TOP_10_FILENAME, na =c("", "NA", "N/A"),show_col_types =FALSE)glimpse(COUNTRY_TOP_10)
# A tibble: 4 × 3
# Groups: category [4]
category show_title total_hours
<chr> <chr> <dbl>
1 Films (English) Red Notice 453990000
2 Films (Non-English) Society of the Snow 235900000
3 TV (English) Stranger Things 2967980000
4 TV (Non-English) Squid Game 5048300000
The single top program overall across all categories is Squid Game with over 5048300000 billion hours of viewership.
5. Which TV show had the longest run in a country’s Top 10? How long was this run and in what country did it occur?
Money Heist: Part 1 achieved the longest Top 10 run of any TV show, spending 127 consecutive weeks in Pakistan Top 10.
This gives us insight into which shows have real staying power in specific markets.
6. Netflix provides over 200 weeks of service history for all but one country in our data set. Which country is this and when did Netflix cease operations in that country?
Russia is the only country in the dataset with fewer than 200 weeks of Netflix service history; the platform operated there for just 35 weeks before ceasing operations on 2022-02-27.
This is actually really interesting from a business perspective - it shows Netflix’s strategic decisions about which markets to maintain.
7. What is the total viewership of the TV show Squid Game? Note that there are three seasons total and we are looking for the total number of hours watched across all seasons?
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# Let's break down Squid Game by seasonsquid_game_breakdown <- GLOBAL_TOP_10 |>filter(str_detect(show_title, "Squid Game")) |>group_by(show_title, season_title) |>summarise(total_hours =sum(weekly_hours_viewed, na.rm =TRUE),hours_millions =round(total_hours /1e6, 1),.groups ='drop' )# Total across all seasonssquid_game_total <- squid_game_breakdown |>summarise(grand_total_hours =sum(total_hours),total_hours_billions =round(sum(total_hours) /1e9, 2) )
Squid Game has been watched for 5.31 billion hours in total across all seasons.
8. The movie Red Notice has a runtime of 1 hour and 58 minutes. Approximately how many views did it receive in 2021?
9. How many Films reached Number 1 in the US but did not originally debut there? That is, find films that first appeared on the Top 10 chart at, e.g., Number 4 but then became more popular and eventually hit Number 1? What is the most recent film to pull this off?
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# Find films that didn't debut at #1 but eventually got thereus_number_ones <- COUNTRY_TOP_10 |>filter(country_name =="United States"&str_detect(category, "Films") & weekly_rank ==1) |>distinct(show_title)climbing_films <- COUNTRY_TOP_10 |>filter(country_name =="United States"&str_detect(category, "Films")) |>filter(show_title %in% us_number_ones$show_title) |>group_by(show_title) |>arrange(week) |>summarise(first_rank =first(weekly_rank),hit_number_1 =any(weekly_rank ==1),latest_week =max(week[weekly_rank ==1], na.rm =TRUE),.groups ='drop' ) |>filter(first_rank >1& hit_number_1) |>arrange(desc(latest_week))print(climbing_films)
# A tibble: 44 × 4
show_title first_rank hit_number_1 latest_week
<chr> <dbl> <lgl> <date>
1 KPop Demon Hunters 6 TRUE 2025-08-24
2 Plane 2 TRUE 2025-06-22
3 The Wild Robot 3 TRUE 2025-06-01
4 Despicable Me 4 2 TRUE 2025-03-09
5 Aftermath 4 TRUE 2025-02-16
6 Despicable Me 2 2 TRUE 2025-01-12
7 Don't Move 3 TRUE 2024-11-03
8 The Garfield Movie 4 TRUE 2024-10-06
9 Uglies 2 TRUE 2024-09-22
10 Jack Reacher: Never Go Back 2 TRUE 2024-08-11
# ℹ 34 more rows
In the United States, 44 films reached Number 1 on the Top 10 chart without originally debuting there, and the most recent film to do so was KPop Demon Hunters.
This shows how word-of-mouth and social media buzz can turn a slow starter into a massive hit.
10. Which TV show/season hit the top 10 in the most countries in its debut week? In how many countries did it chart?
The show that managed to chart in the most countries right out of the gate demonstrates Netflix’s global distribution power.
Writing Press Releases Based on the Data
Press Release 1: Upcoming Season of Stranger Things
Netflix Announces Final Season of Stranger Things: A Global Phenomenon Reaches Its Conclusion
Netflix today confirmed that the upcoming fifth season of its hit series Stranger Things will mark the conclusion of one of the platform’s most successful franchises. Since its debut in 2016, Stranger Things has grown beyond a television series to become a cultural touchstone, captivating audiences worldwide.
According to Netflix’s global viewing data, the series has generated approximately 2.97billion total viewing hours across its four released seasons and reached audiences in 93 countries. Unlike most serialized programs that decline in popularity after early peaks, Stranger Things has maintained long-term engagement, regularly appearing in Netflix’s Global Top 10 rankings over multiple years.
From a data perspective, the longevity of Stranger Things is particularly noteworthy. Most serialized television experiences a steep decline in viewership after initial release peaks. In contrast, this series has consistently appeared in Netflix’s global “Top 10” rankings over multiple years, signaling an unusual level of audience loyalty and sustained cultural relevance.
“Stranger Things is more than a story about Hawkins — it’s a story that connected generations and cultures,” said [Insert Netflix Executive Quote]. “The final season represents not only the closing chapter of this incredible narrative but also a moment to celebrate the lasting impact the show has had on fans everywhere.”
Analysts point to three factors behind the show’s sustained success:
High Retention: Each new season sparked renewed spikes in viewership rather than a single early peak.
Global Reach: With consistent performance across diverse markets, the series demonstrated cross-cultural appeal.
Platform Strategy: Netflix’s binge-release model encouraged high-volume viewing while building brand loyalty.
The final season of Stranger Things will premiere in [Insert Year]. For Netflix, this marks a pivotal milestone: proof that streaming originals can achieve not only critical acclaim but also global cultural permanence.
Press Release 2: Netflix’s India Strategy Actually Works
Netflix’s Hindi Originals Drive Subscriber Growth in India
Netflix today announced that its investment in Hindi-language content has fueled significant subscriber growth in India, one of the company’s fastest-expanding markets. Over the past two years, Netflix’s Indian subscriber base has grown from an estimated 15 million to more than 25 million, underscoring the value of local programming tailored to cultural preferences.
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# Looking at Hindi content performance in India specificallyhindi_content <- COUNTRY_TOP_10 %>%filter(country_name =="India", str_detect(show_title, "Hindi")) %>%group_by(show_title) %>%summarise(weeks_in_top10 =max(cumulative_weeks_in_top_10, na.rm =TRUE), .groups ='drop') %>%arrange(desc(weeks_in_top10))plot_ly( hindi_content %>%slice_head(n =10),x =~reorder(show_title, -weeks_in_top10),y =~weeks_in_top10,type ='bar',marker =list(color ='#E50914'),text =~weeks_in_top10,hoverinfo ='text+y') %>%layout(title ="Top Hindi Programs in India",xaxis =list(title ="", tickangle =-45),yaxis =list(title ="Weeks in Top 10"),margin =list(b =150) )
Data from Netflix’s Top 10 charts shows that Hindi-language series consistently dominate in India while rarely appearing in U.S. rankings. This localized success highlights a deliberate strategy: producing authentic, market-specific stories rather than relying on globally standardized content.
“Our Hindi originals demonstrate that audiences in India are eager for stories that reflect their lives, languages, and communities,” said [Insert Netflix India Executive Quote]. “By investing in local creators, we’re building a sustainable growth model and establishing Netflix as the home for India’s most compelling entertainment.”
Industry analysts note that this approach offers two key competitive advantages:
Market Differentiation: By prioritizing original Hindi programming, Netflix stands apart from competitors that focus on dubbed or imported content.
Scalable Growth: Success in India positions Netflix to replicate similar local-first strategies in other emerging markets.
With India projected to remain a critical driver of subscriber additions in the coming years, Netflix’s investment in Hindi-language storytelling underscores its long-term commitment to regional markets and cultural authenticity.
Netflix today reported that non-English programming now accounts for more than 40% of global viewing hours, marking a dramatic shift in entertainment consumption patterns worldwide. Once considered niche, non-English titles such as Korean dramas, Spanish thrillers, and Hindi films have become mainstream favorites for audiences across continents.
Year-over-year data highlights explosive growth: the number of international titles has expanded to over 2,500, spanning 35 producing countries and 45 languages, with 180 cross-cultural hits achieving global recognition.
“Audiences today are embracing stories from around the world in ways we’ve never seen before,” said [Insert Netflix Content Executive Quote]. “Our job is to make great local stories available everywhere, and increasingly those stories are finding fans far beyond their home markets.”
Analysts attribute the trend to Netflix’s recommendation algorithms, which introduce subscribers to content they might not have previously considered. By promoting localized hits internationally, the platform has redefined how entertainment is distributed and consumed.
The rise of non-English content has major business implications:
Diversified Growth: Reliance on global hits reduces risk compared to single-region blockbusters.
Algorithmic Advantage: Netflix’s data-driven curation ensures strong titles can scale rapidly across markets.
Cultural Exchange: Subtitled and dubbed formats normalize cross-cultural viewing, expanding audience horizons.
This shift positions Netflix not only as a streaming service but also as a global hub for cultural storytelling, reshaping the very definition of mainstream entertainment.
Stranger Things proves Netflix can create genuine cultural phenomena - not just popular shows, but things that become part of how people talk and think about entertainment.
The India strategy shows that local investment actually works - rather than just dubbing American content, creating authentic local programming builds real market share.
Non-English content has gone mainstream - what used to be a niche preference is now driving nearly half of global viewing hours.
The implications for Netflix’s business model are pretty significant. Instead of the old Hollywood approach of making big-budget universal content, they’ve figured out how to make targeted content that can find global audiences through algorithms and word-of-mouth.
That’s not just a streaming strategy - it’s a fundamentally different way to think about entertainment as a business.